Epsilon Open Archive

Abstract

Genetic testing plays an important role in tree breeding. Understanding basic genetic parameters for growth and fertility is a prerequisite for developing a successful breeding strategy to balance between short- and long-term gains. It is both impractical and inefficient to wait until trials are several decades old to make selection decisions. It is therefore of great value to evaluate a sample of long-term experiments to confirm selection efficiency at ages closer to rotation time. This thesis investigates the genetic expression of field performance in Scots pine (Pinus sylvestris L.) for growth and fertility in long-term experiments over time and across sites.

The data revealed large variation in genetic expression over time and across sites. The additive effect was the most important source of genetic variation for growth, while estimates of dominance were small, site-specific, and decreased much with age. Thus, there is little benefit in attempting to explore dominance through specific combining ability to improve genetic productivity in northern Sweden.

Progeny of plus-trees selected in northern Sweden showed faster growth compared to unimproved controls at age 30, as well as genetic differences in stem shape, such that improved trees were more slender. The genetic relationship between height and diameter in northern Sweden demonstrated the importance of considering diameter in selection to obtain greater genetic gain in volume.

Overall, the results show higher heritability at older ages and genetic correlations that reveal important rank changes with time and across environments.

Clonal variation in female fertility in mature seed orchards is rather small and varies as much within clones as among clones, and is heavily dependent on year of assessment. The prospects for early selection of clones for future seed cone production based on a single-year observation are low.

Finally, this thesis illustrates the importance of subjecting data from long-term field tests to a multi-trait, multi-site analysis accounting for environmental effects.